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预测结直肠癌患者的生存率:多基因生存评分的开发与验证

Predicting Survival Among Colorectal Cancer Patients: Development and Validation of Polygenic Survival Score.

作者信息

Maawadh Rawan M, Xu Chao, Ahmed Rizwan, Mushtaq Nasir

机构信息

Clinical Laboratory Science Department, Prince Sultan Military College of Health Science, Dammam, Saudi Arabia.

Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.

出版信息

Clin Exp Gastroenterol. 2024 Oct 14;17:317-329. doi: 10.2147/CEG.S464324. eCollection 2024.


DOI:10.2147/CEG.S464324
PMID:39431218
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11488504/
Abstract

PURPOSE: Colorectal cancer is the second leading cause of cancer-related death in the United States. A multi-omics approach has contributed in identifying various cancer-specific mutations, epigenetic alterations, and cells response to chemotherapy. This study aimed to determine the factors associated with colorectal cancer survival and develop and validate a polygenic survival scoring system (PSS) using a multi-omics approach. PATIENTS AND METHODS: Data were obtained from the Cancer Genome Atlas (TCGA). Colon Adenocarcinoma (TCGA-COAD) data were used to develop a survival prediction model and PSS, whereas rectal adenocarcinoma (TCGA-READ) data were used to validate the PSS. Cox proportional hazards regression analysis was conducted to examine the association between the demographic characteristics, clinical variables, and mRNA gene expression. RESULTS: Overall accuracy of PSS was also evaluated. The median overall survival for TCGA-COAD patients was 7 years and for TCGA-READ patients was 5 years. The multivariate Cox proportional hazards model identified age, cancer stage, and expression of nine genes as predictors of colon cancer survival. Based on the median PSS of 0.38, 48% of TCGA-COAD patients had high mortality risk. Patients in the low risk group had significantly higher 5-year survival rates than those in the high group (p <0.0001). The PSS demonstrated a high overall accuracy in predicting colorectal cancer survival. CONCLUSION: This study integrated clinical and transcriptome data to identify survival predictors in patients with colorectal cancer. PSS is an accurate and valid measure for estimating colorectal cancer survival. Thus, it can serve as an important tool for future colorectal cancer research.

摘要

目的:结直肠癌是美国癌症相关死亡的第二大主要原因。多组学方法有助于识别各种癌症特异性突变、表观遗传改变以及细胞对化疗的反应。本研究旨在确定与结直肠癌生存相关的因素,并使用多组学方法开发和验证一种多基因生存评分系统(PSS)。 患者与方法:数据来自癌症基因组图谱(TCGA)。结肠腺癌(TCGA-COAD)数据用于开发生存预测模型和PSS,而直肠腺癌(TCGA-READ)数据用于验证PSS。进行Cox比例风险回归分析以检查人口统计学特征、临床变量和mRNA基因表达之间的关联。 结果:还评估了PSS的总体准确性。TCGA-COAD患者的中位总生存期为7年,TCGA-READ患者为5年。多变量Cox比例风险模型确定年龄、癌症分期和9个基因的表达为结肠癌生存的预测因素。基于0.38的中位PSS,48%的TCGA-COAD患者具有高死亡风险。低风险组患者的5年生存率显著高于高风险组(p<0.0001)。PSS在预测结直肠癌生存方面显示出较高的总体准确性。 结论:本研究整合了临床和转录组数据以识别结直肠癌患者的生存预测因素。PSS是评估结直肠癌生存的准确且有效的指标。因此,它可作为未来结直肠癌研究的重要工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7215/11488504/d663e2395d32/CEG-17-317-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7215/11488504/46546ed3ba51/CEG-17-317-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7215/11488504/8aae43764948/CEG-17-317-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7215/11488504/8472910bc603/CEG-17-317-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7215/11488504/7aac96a67703/CEG-17-317-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7215/11488504/d663e2395d32/CEG-17-317-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7215/11488504/46546ed3ba51/CEG-17-317-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7215/11488504/8aae43764948/CEG-17-317-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7215/11488504/8472910bc603/CEG-17-317-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7215/11488504/7aac96a67703/CEG-17-317-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7215/11488504/d663e2395d32/CEG-17-317-g0005.jpg

相似文献

[1]
Predicting Survival Among Colorectal Cancer Patients: Development and Validation of Polygenic Survival Score.

Clin Exp Gastroenterol. 2024-10-14

[2]
Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality.

EBioMedicine. 2023-8

[3]
Spatially aware graph neural networks and cross-level molecular profile prediction in colon cancer histopathology: a retrospective multi-cohort study.

Lancet Digit Health. 2022-11

[4]
Transcriptomic correlates of cell cycle checkpoints with distinct prognosis, molecular characteristics, immunological regulation, and therapeutic response in colorectal adenocarcinoma.

Front Immunol. 2023

[5]
Comprehensive Analysis of Gene Expression Profiles Identifies a -Related Gene Panel as a Prognostic Model in Colorectal Cancer Patients.

Cancer Biother Radiopharm. 2021-10

[6]
Comprehensive investigation of a novel differentially expressed lncRNA expression profile signature to assess the survival of patients with colorectal adenocarcinoma.

Oncotarget. 2017-3-7

[7]
Glucose metabolism-based signature predicts prognosis and immunotherapy strategies for colon adenocarcinoma.

J Gene Med. 2024-1

[8]
An Intratumor Heterogeneity-Related Signature for Predicting Prognosis, Immune Landscape, and Chemotherapy Response in Colon Adenocarcinoma.

Front Med (Lausanne). 2022-7-7

[9]
Construction of a prognostic risk model of colorectal adenocarcinoma through integrated analysis of RNA-binding proteins.

Transl Cancer Res. 2021-5

[10]
Prognostic implications of ferroptosis-associated gene signature in colon adenocarcinoma.

World J Clin Cases. 2021-10-16

本文引用的文献

[1]
Annual Report to the Nation on the Status of Cancer, Part 1: National Cancer Statistics.

J Natl Cancer Inst. 2021-11-29

[2]
New insights of the correlation between AXIN2 polymorphism and cancer risk and susceptibility: evidence from 72 studies.

BMC Cancer. 2021-4-1

[3]
Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.

CA Cancer J Clin. 2021-5

[4]
Evaluating the Utility of Polygenic Risk Scores in Identifying High-Risk Individuals for Eight Common Cancers.

JNCI Cancer Spectr. 2020-3-12

[5]
Polygenic risk scores: from research tools to clinical instruments.

Genome Med. 2020-5-18

[6]
High expression of Krüppel-like factor 5 is associated with poor prognosis in patients with colorectal cancer.

Cancer Sci. 2020-5-5

[7]
Multi-omics Data Integration, Interpretation, and Its Application.

Bioinform Biol Insights. 2020-1-31

[8]
Gene expression based survival prediction for cancer patients-A topic modeling approach.

PLoS One. 2019-11-15

[9]
Variations in predict risk and prognosis of colorectal cancer.

BDJ Open. 2019-10-16

[10]
Overall survival of colorectal cancer by stage at diagnosis: Data from the Martinique Cancer Registry.

Medicine (Baltimore). 2019-8

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